CCEE PPA Definitions

CceePpaDefinitions(perfdb)

Class used for handling CCEE PPA definitions. Can be accessed via perfdb.ccee.ppa.definitions.

Parameters:

• perfdb

  (PerfDB) –

  Top level object carrying all functionality and the connection handler.

Source code in echo_postgres/perfdb_root.py

Python

def __init__(self, perfdb: e_pg.PerfDB) -> None:
    """Base class that all subclasses should inherit from.

    Parameters
    ----------
    perfdb : PerfDB
        Top level object carrying all functionality and the connection handler.

    """
    self._perfdb: e_pg.PerfDB = perfdb

get(spes=None, market_types=None, filter_type='and', output_type='DataFrame')

Gets PPA definitions from the database.

The definitions do not contain adjusted values. The values presented here are the base when the PPA was signed.

Them main keys/columns returned are:

• spe_name
• submarket_acronym
• market_type_name
• start
• end
• price
• base_date
• price_adjustment_month
• sold_mw_avg

Parameters:

• spes

  (list[str], default: None ) –

  List of SPES to get the PPA definitions from. By default None, which means all SPES.

• market_types

  (list[str], default: None ) –

  List of market types (Free or Regulated) to get the PPA definitions from. By default None, which means all market types.

• filter_type

  (Literal['and', 'or'], default: 'and' ) –

  How to treat multiple filters. Can be one of ["and", "or"]. By default "and"

• output_type

  (Literal['dict', 'DataFrame', 'pl.DataFrame'], default: 'DataFrame' ) –

  Output type of the data. Can be one of ["dict", "DataFrame", "pl.DataFrame"] By default "DataFrame"

Returns:

• DataFrame –

  In case output_type = 'DataFrame', returns a pandas DataFrame with the PPA definitions. The index will be a Multindex with the levels: spe_name and start. Columns will have the other attributes.

• DataFrame –

  In case output_type is "pl.DataFrame", returns a Polars DataFrame with the PPA definitions.

• dict[str, dict[datetime, Any]] –

  In case output_type = 'dict', returns a dictionary with the PPA definitions. The structure is {spe_name: {start: {attribute: value, ...}, ...}, ...}

Source code in echo_postgres/ccee_ppa_definitions.py

Python

@validate_call
def get(
    self,
    spes: list[str] | None = None,
    market_types: list[str] | None = None,
    filter_type: Literal["and", "or"] = "and",
    output_type: Literal["dict", "DataFrame", "pl.DataFrame"] = "DataFrame",
) -> pd.DataFrame | pl.DataFrame | dict[str, dict[datetime, Any]]:
    """Gets PPA definitions from the database.

    The definitions do not contain adjusted values. The values presented here are the base when the PPA was signed.

    Them main keys/columns returned are:

    - spe_name
    - submarket_acronym
    - market_type_name
    - start
    - end
    - price
    - base_date
    - price_adjustment_month
    - sold_mw_avg

    Parameters
    ----------
    spes : list[str], optional
        List of SPES to get the PPA definitions from. By default None, which means all SPES.
    market_types : list[str], optional
        List of market types (Free or Regulated) to get the PPA definitions from. By default None, which means all market types.
    filter_type : Literal["and", "or"]
        How to treat multiple filters. Can be one of ["and", "or"]. By default "and"
    output_type : Literal["dict", "DataFrame", "pl.DataFrame"], optional
        Output type of the data. Can be one of ["dict", "DataFrame", "pl.DataFrame"]
        By default "DataFrame"

    Returns
    -------
    pd.DataFrame
        In case output_type = 'DataFrame', returns a pandas DataFrame with the PPA definitions. The index will be a Multindex with the levels: spe_name and start. Columns will have the other attributes.
    pl.DataFrame
        In case output_type is "pl.DataFrame", returns a Polars DataFrame with the PPA definitions.
    dict[str, dict[datetime, Any]]
        In case output_type = 'dict', returns a dictionary with the PPA definitions. The structure is {spe_name: {start: {attribute: value, ...}, ...}, ...}
    """
    # checking inputs
    if not isinstance(spes, list | type(None)):
        raise TypeError(f"spes must be a list of str, got {type(spes)}")
    if not isinstance(market_types, list | type(None)):
        raise TypeError(f"market_types must be a list of str, got {type(market_types)}")

    # defining the query
    query = [sql.SQL("SELECT * FROM v_ccee_ppa_def")]
    where_query = []
    if spes:
        where_query.append(sql.SQL("spe_name IN ({spes})").format(spes=sql.SQL(", ").join(map(sql.Literal, spes))))
    if market_types:
        where_query.append(
            sql.SQL("market_type_name IN ({market_types})").format(market_types=sql.SQL(", ").join(map(sql.Literal, market_types))),
        )
    if where_query:
        where_query = sql.SQL(" WHERE ") + sql.SQL(f" {filter_type.upper()} ").join(where_query)
        query.append(where_query)
    query = sql.Composed(query)

    # getting the data
    df = self._perfdb.conn.read_to_polars(query)

    return convert_output(
        df,
        output_type=output_type,
        index_col=["spe_name", "start"],
        nest_by_index=True,
    )